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Hybrid Neuro-Fuzzy Network Identification for Autonomous Underwater Vehicles

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Book cover Swarm, Evolutionary, and Memetic Computing (SEMCCO 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8298))

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Abstract

Autonomous Underwater Vehicles (AUVs) are ideal platforms for aquatic search and rescue operations and exploration. The AUV poses serious challenges due to its complex, inherently nonlinear and time-varying dynamics. In addition, its hydrodynamic coefficients are difficult to model accurately because of their variations under different navigational conditions and manoeuvring in uncertain environments. This paper introduces an identifier scheme for identification of non-linear systems with disturbances based on Hybrid Neuro-Fuzzy Network (HNFN) technique. The method comprises of an automatic structure-generating phase using entropy based technique. The accuracy of the model is suitably controlled using the entropy measure. To improve the accuracy and also for generalization of the model to handle different data sets, Differential Evolution technique (DE) is employed. Finally, Hardware In-Loop (HIL) simulation and real-time experiments using the proposed algorithm to identify the 6-DOF UNSW Canberra AUV’s dynamics are implemented. The modelling performance and generalisation capability are seen to be superior with our method.

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Author information

Authors and Affiliations

  1. School of Engineering and Information Technology, UNSW@ADFA, Canberra, ACT, Australia

    Osama Hassanein, G. Sreenatha & Tapabrata Ray

Authors
  1. Osama Hassanein
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  2. G. Sreenatha
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  3. Tapabrata Ray
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Editor information

Editors and Affiliations

  1. Electrical Engineering Dept, IIT Delhi, Hauz Khas, 110016, New Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, 700108, Kolkata, India

    Swagatam Das

  4. SRM Engineering College, Department of Electrical and Electronics Engineering, SRM University, Potheri, Kattankulathur, 603203, Kancheepuram, Tamil Nadu, India

    Shubhransu Sekhar Dash

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Hassanein, O., Sreenatha, G., Ray, T. (2013). Hybrid Neuro-Fuzzy Network Identification for Autonomous Underwater Vehicles. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8298. Springer, Cham. https://doi.org/10.1007/978-3-319-03756-1_26

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  • DOI: https://doi.org/10.1007/978-3-319-03756-1_26

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03755-4

  • Online ISBN: 978-3-319-03756-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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